DATA DRIVEN MANUFACTURING WITH IIOT
SKZ optimizes processes and improves product quality with unprecedented machine data insights
Case Study Highlights
Unmatched product quality through machine learning
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Shopfloor data available in real-time
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Significant decrease in rejects
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Improved product quality
INDUSTRY
Plastic solutions
EMPLOYEES
430+
PRODUCTION SITES
6
Challenges
Lack of data lowers production quality and increases waste
- Narrow temperature window for optimum product quality
- Inaccurate measurement due to lack of sensors in the centre of the melt
- High reject rate and lower production quality
Solution
Machine learning model provides data insights
- Integration and centralization of data from various sources and sensors using Cybus Connectware
- Machine learning model based on the data obtained
- Deriving recommendations via a visual dashboard
CYBUS CONNECTWARE
Scalable data architecture for digital manufacturing
- End-to-end data transparency and OT-IT integration – for every step towards smart factory transformation
- Scalable data architecture for digital projects without limits
- All IIoT projects integrated by a single software – independent of interfaces and manufacturers
BENEFITS
A better understanding of the production process is the key to first-class product quality
Shopfloor data available
The machine learning model and real-time availability of shop floor data is a significant leap in optimizing plastic production processes.
Decrease in rejects
Obtaining and processing crucial machine data that was previously inaccessible leads to more efficient and higher quality production with significantly less rejects.
Improved product quality
This advancement optimizes products and solves market challenges, advancing intelligent manufacturing in the plastics industry.
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